住房抵押贷款中的排队、服务时间和价格动态

Federal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print) Queuing, Service Time, and Price Dynamics in ResidentialMortgage Lending Akos Horvath, Benjamin S. Kay 2026-017 Please cite this paper as:Horvath,Akos,and Benjamin S.Kay(2026).“Queuing,Service Time,and PriceDynamicsin Residential Mortgage Lending,”Finance and Economics Discussion Se-ries2026-017.Washington:Boardof Governors of the Federal Reserve System,https://doi.org/10.17016/FEDS.2026.017. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment.The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or the Queuing, Service Time, and Price Dynamics inResidential Mortgage Lending∗ Akos HorvathFederal Reserve Board March 2026 Abstract Building on queuing theory, we develop and empirically validate a novel theoretical modelof residential mortgage supply.Our model gives insight into how the stochastic arrival andsequential servicing of loan applications affect mortgage origination. The model provides closed-form predictions for lenders’ optimal response to changes in the level and price elasticity ofmortgage demand. Using confidential HMDA data, we estimate that a one standard deviation 1Introduction Positive shocks to mortgage demand can cause a significant increase in lender markups because ofcapacity constraints (Scharfstein and Sunderam, 2016; Fuster, Lo and Willen, 2024).1The empirical evidence for this effect is circumstantial: markups are higher at times when the aggregate quantityof mortgage applications is higher (Fuster et al., 2021; Fuster, Lo and Willen, 2024) or in countieswith more concentrated mortgage markets (Scharfstein and Sunderam, 2016). However, the finance Our paper investigates three related questions. First, how do mortgage lenders adjust theirrates when they experience a positive demand shock and their capacity is limited? Second, howdoes the equilibrium quantity of mortgages change because of the demand shock and the resulting We make two contributions to the finance literature. First, we introduce a novel structural modelof mortgage lender capacity constraints. Second, we empirically identify the model’s parameters formortgage demand and test its predictions at the individual lender level to study the effect of such In our structural model, we embed a stochastic (M/M/1) queuing model in a standard profit-maximization framework to capture the effect of lender capacity constraints on mortgage rates,quantities, and processing times. In the model, queue length represents operational congestion, whichdirectly affects borrower waiting times and indirectly affects borrower willingness to pay. To our In our empirical analysis, we apply our structural model at the lender level to investigate howobserved mortgage outcomes change in response to variation in mortgage demand. We use loan-leveldata from the confidential Home Mortgage Disclosure Act collection, which includes applicationand origination dates, loan amounts and rates, and borrower characteristics. The analysis proceedsin two steps. In step one, we identify lender-specific mortgage demand curves by using shifts in We find that mortgage lenders respond to demand shocks consistent with the theoreticalpredictions of our model with capacity constraints. Specifically, we estimate that a one standarddeviation increase in demand raises mortgage spreads by 3 to 8 basis points, increases loan quantitiesby 20 to 31 percent, and extends processing times by 3 to 5 days. These findings indicate that We also find that borrower price sensitivity attenuates the increase in lender pricing power inresponse to demand shocks. Specifically, we estimate that a one standard deviation increase inthe elasticity of mortgage demand reduces spreads by 0.7 to 2.4 basis points. Because demandshocks and elasticity are positively correlated in our analysis, this interaction moderates changes Our regression estimates are robust across specifications (over a variety of fixed effects, controls,and quadratic terms), and their signs are in line with our theoretical model predictions. Importantly,the empirical results indicate that identification is primarily driven by time-series rather thancross-sectional variation, suggesting that macroeconomic factors, such as interest rate cycles, arekey determinants of mortgage demand dynamics. Indeed, once we control for time fixed effects, the While we focus on the residential mortgage market, our methodology can be applied more broadlyto other markets in which firms face stochastic demand and limited processing capacity. Marketsettings such as small business lending, post-disaster reconstruction, commercial aviation, and The paper is structured as follows.Section 2 introduces our theoretical model.Section 3describes the data used in the empirical analysis. Section